SoroushVahidi/scidocs huggingface dataset:
What's in an Explanation? Characterizing Knowledge and Inference Requirements for Elementary Science Exams
(2016)

QA systems have been making steady advances in the challenging elementary science exam domain. In this work, we develop an explanation-based analysis of knowledge and inference requirements, which supports a fine-grained characterization of the challenges. In particular, we model the requirements based on appropriate sources of evidence to be used for the QA task. We create requirements by first identifying suitable sentences in a knowledge base that support the correct answer, then use these to build explanations, filling in any necessary missing information. These explanations are used to create a fine-grained categorization of the requirements. Using these requirements, we compare a retrieval and an inference solver on 212 questions. The analysis validates the gains of the inference solver, demonstrating that it answers more questions requiring complex inference, while also providing insights into the relative strengths of the solvers and knowledge sources. We release the annotated questions and explanations as a resource with broad utility for science exam QA, including determining knowledge base construction targets, as well as supporting information aggregation in automated inference.
Is imitation learning the route to humanoid robots?
(1999)

This review investigates two recent developments in artificial intelligence and neural computation: learning from imitation and the development of humanoid robots. It is postulated that the study of imitation learning offers a promising route to gain new insights into mechanisms of perceptual motor control that could ultimately lead to the creation of autonomous humanoid robots. Imitation learning focuses on three important issues: efficient motor learning, the connection between action and perception, and modular motor control in the form of movement primitives. It is reviewed here how research on representations of, and functional connections between, action and perception have contributed to our understanding of motor acts of other beings. The recent discovery that some areas in the primate brain are active during both movement perception and execution has provided a hypothetical neural basis of imitation. Computational approaches to imitation learning are also described, initially from the perspective of traditional AI and robotics, but also from the perspective of neural network models and statistical-learning research. Parallels and differences between biological and computational approaches to imitation are highlighted and an overview of current projects that actually employ imitation learning for humanoid robots is given.
A survey of robot learning from demonstration
(2009)

We present a comprehensive survey of robot Learning from Demonstration (LfD), a technique that develops policies from example state to action mappings. We introduce the LfD design choices in terms of demonstrator, problem space, policy derivation and performance, and contribute the foundations for a structure in which to categorize LfD research. Specifically, we analyze and categorize the multiple ways in which examples are gathered, ranging from teleoperation to imitation, as well as the various techniques for policy derivation, including matching functions, dynamics models and plans. To conclude we discuss LfD limitations and related promising areas for future research.
Learning Dependency-Based Compositional Semantics
(2011)

Suppose we want to build a system that answers a natural language question by representing its semantics as a logical forxm and computing the answer given a structured database of facts. The core part of such a system is the semantic parser that maps questions to logical forms. Semantic parsers are typically trained from examples of questions annotated with their target logical forms, but this type of annotation is expensive.Our goal is to instead learn a semantic parser from question–answer pairs, where the logical form is modeled as a latent variable. We develop a new semantic formalism, dependency-based compositional semantics (DCS) and define a log-linear distribution over DCS logical forms. The model parameters are estimated using a simple procedure that alternates between beam search and numerical optimization. On two standard semantic parsing benchmarks, we show that our system obtains comparable accuracies to even state-of-the-art systems that do require annotated logical forms.
Understanding physiological responses to stressors during physical activity
(2012)

With advances in physiological sensors, we are able to understand people's physiological status and recognize stress to provide beneficial services. Despite the great potential in physiological stress recognition, there are some critical issues that need to be addressed such as the sensitivity and variability of physiology to many factors other than stress (e.g., physical activity). To resolve these issues, in this paper, we focus on the understanding of physiological responses to both stressor and physical activity and perform stress recognition, particularly in situations having multiple stimuli: physical activity and stressors. We construct stress models that correspond to individual situations, and we validate our stress modeling in the presence of physical activity. Analysis of our experiments provides an understanding on how physiological responses change with different stressors and how physical activity confounds stress recognition with physiological responses. In both objective and subjective settings, the accuracy of stress recognition drops by more than 14% when physical activity is performed. However, by modularizing stress models with respect to physical activity, we can recognize stress with accuracies of 82% (objective stress) and 87% (subjective stress), achieving more than a 5-10% improvement from approaches that do not take physical activity into account.
Identifying Learning Styles in Learning Management Systems by Using Indications from Students' Behaviour
(2008)

Making students aware of their learning styles and presenting them with learning material that incorporates their individual learning styles has potential to make learning easier for students and increase their learning progress. This paper proposes an automatic approach for identifying learning styles with respect to the Felder-Silverman learning style model by inferring their learning styles from their behaviour during they are learning in an online course. The approach was developed for learning management systems, which are commonly used in e-learning. In order to evaluate the proposed approach, a study with 127 students was performed, comparing the results of the automatic approach with those of a learning style questionnaire. The evaluation yielded good results and demonstrated that the proposed approach is suitable for identifying learning styles. By using the proposed approach, studentspsila learning styles can be identified automatically and be used for supporting students by considering their individual learning styles.
Disentangling top-down vs. bottom-up and low-level vs. high-level influences on eye movements over time
(2018)

Bottom-up and top-down, as well as low-level and high-level factors influence where we fixate when viewing natural scenes. However, the importance of each of these factors and how they interact remains a matter of debate. Here, we disentangle these factors by analysing their influence over time. For this purpose we develop a saliency model which is based on the internal representation of a recent early spatial vision model to measure the low-level bottom-up factor. To measure the influence of high-level bottom-up features, we use a recent DNN-based saliency model. To account for top-down influences, we evaluate the models on two large datasets with different tasks: first, a memorisation task and, second, a search task. Our results lend support to a separation of visual scene exploration into three phases: The first saccade, an initial guided exploration characterised by a gradual broadening of the fixation density, and an steady state which is reached after roughly 10 fixations. Saccade target selection during the initial exploration and in the steady state are related to similar areas of interest, which are better predicted when including high-level features. In the search dataset, fixation locations are determined predominantly by top-down processes. In contrast, the first fixation follows a different fixation density and contains a strong central fixation bias. Nonetheless, first fixations are guided strongly by image properties and as early as 200ms after image onset, fixations are better predicted by high-level information. We conclude that any low-level bottom-up factors are mainly limited to the generation of the first saccade. All saccades are better explained when high-level features are considered, and later this high-level bottom-up control can be overruled by top-down influences.
Workspace characterization for concentric tube continuum robots
(2014)

Concentric tube robots exhibit complex workspaces due to the way their component tubes bend and twist as they interact with one another. This paper explores ways to compute and characterize their workspaces. We use Monte Carlo random samples of the robot's joint space and a discrete volumetric workspace representation, which can describe both reachability and redundancy. Experiments on two physical prototypes are provided to illustrate the proposed approach.
The use of vacuum assisted closure (VAC™) in soft tissue injuries after high energy pelvic trauma
(2006)

Application of vacuum-assisted closure (VAC™) in soft tissue defects after high-energy pelvic trauma is described as a retrospective study in a level one trauma center. Between 2002 and 2004, 13 patients were treated for severe soft tissue injuries in the pelvic region. All musculoskeletal injuries were treated with multiple irrigation and debridement procedures and broad-spectrum antibiotics. VAC™ was applied as a temporary coverage for defects and wound conditioning. The injuries included three patients with traumatic hemipelvectomies. Seven patients had pelvic ring fractures with five Morel–Lavallee lesions and two open pelviperineal trauma. One patient suffered from an open iliac crest fracture and a Morel–Lavallee lesion. Two patients sustained near complete pertrochanteric amputations of the lower limb. The average injury severity score was 34.1 ± 1.4. The application of VAC™ started in average 3.8 ± 0.4 days after trauma and was used for 15.5 ± 1.8 days. The dressing changes were performed in average every 3 days. One patient (8%) with a traumatic hemipelvectomy died in the course of treatment due to septic complications. High-energy trauma causing severe soft tissues injuries requires multiple operative debridements to prevent high morbidity and mortality rates. The application of VAC™ as temporary coverage of large tissue defects in pelvic regions supports wound conditioning and facilitates the definitive wound closure.
Evaluating the Impact of Social Selfishness on the Epidemic Routing in Delay Tolerant Networks
(2010)

To cope with the uncertainty of transmission opportunities between mobile nodes, Delay Tolerant Networks (DTN) routing exploits opportunistic forwarding mechanism. This mechanism requires nodes to forward messages in a cooperative and altruistic way. However, in the real world, most of the nodes exhibit selfish behaviors such as individual and social selfishness. In this paper, we investigate the problem of how social selfishness influences the performance of epidemic routing in DTN. First, we model the message delivery process with social selfishness as a two dimensional continuous time Markov chain. Then, we obtain the system performance of message delivery delay and delivery cost by explicit expressions. Numerical results show that DTN is quite robust to social selfishness, which increases the message delivery delay, but there is more reducing of delivery cost.
Crowdsourcing for book search evaluation: impact of hit design on comparative system ranking
(2011)

The evaluation of information retrieval (IR) systems over special collections, such as large book repositories, is out of reach of traditional methods that rely upon editorial relevance judgments. Increasingly, the use of crowdsourcing to collect relevance labels has been regarded as a viable alternative that scales with modest costs. However, crowdsourcing suffers from undesirable worker practices and low quality contributions. In this paper we investigate the design and implementation of effective crowdsourcing tasks in the context of book search evaluation. We observe the impact of aspects of the Human Intelligence Task (HIT) design on the quality of relevance labels provided by the crowd. We assess the output in terms of label agreement with a gold standard data set and observe the effect of the crowdsourced relevance judgments on the resulting system rankings. This enables us to observe the effect of crowdsourcing on the entire IR evaluation process. Using the test set and experimental runs from the INEX 2010 Book Track, we find that varying the HIT design, and the pooling and document ordering strategies leads to considerable differences in agreement with the gold set labels. We then observe the impact of the crowdsourced relevance label sets on the relative system rankings using four IR performance metrics. System rankings based on MAP and Bpref remain less affected by different label sets while the Precision@10 and nDCG@10 lead to dramatically different system rankings, especially for labels acquired from HITs with weaker quality controls. Overall, we find that crowdsourcing can be an effective tool for the evaluation of IR systems, provided that care is taken when designing the HITs.
Information quality benchmarks: product and service performance
(2002)

Information quality (IQ) is an inexact science in terms of assessment and benchmarks. Although various aspects of quality and information have been investigated [1, 4, 6, 7, 9, 12], there is still a critical need for a methodology that assesses how well organizations develop information products and deliver information services to consumers. Benchmarks developed from such a methodology can help compare information quality across organizations, and provide a baseline for assessing IQ improvements.
Recoding, storage, rehearsal and grouping in verbal short-term memory: an fMRI study
(2000)

Functional magnetic resonance imaging (fMRI) of healthy volunteers is used to localise the processes involved in verbal short-term memory (VSTM) for sequences of visual stimuli. Specifically, the brain areas underlying (i) recoding, (ii) storage, (iii) rehearsal and (iv) temporal grouping are investigated. Successive subtraction of images obtained from five tasks revealed a network of left-lateralised areas, including posterior temporal regions, supramarginal gyri, Broca's area and dorsolateral premotor cortex. The results are discussed in relation to neuropsychological distinctions between recoding and rehearsal, previous neuroimaging studies of storage and rehearsal, and, in particular, a recent connectionist model of VSTM that makes explicit assumptions about the temporal organisation of rehearsal. The functional modules of this model are tentatively mapped onto the brain in light of the imaging results. Our findings are consistent with the representation of verbal item information in left posterior temporal areas and short-term storage of phonological information in left supramarginal gyrus. They also suggest that left dorsolateral premotor cortex is involved in the maintenance of temporal order, possibly as the location of a timing signal used in the rhythmic organisation of rehearsal, whereas Broca's area supports the articulatory processes required for phonological recoding of visual stimuli.
Discourse Annotation of Non-native Spontaneous Spoken Responses Using the Rhetorical Structure Theory Framework
(2017)

The availability of the Rhetorical Structure Theory (RST) Discourse Treebank has spurred substantial research into discourse analysis of written texts; however, limited research has been conducted to date on RST annotation and parsing of spoken language, in particular, nonnative spontaneous speech. Considering that the measurement of discourse coherence is typically a key metric in human scoring rubrics for assessments of spoken language, we initiated a research effort to obtain RST annotations of a large number of non-native spoken responses from a standardized assessment of academic English proficiency. The resulting inter-annotator κ agreements on the three different levels of Span, Nuclearity, and Relation are 0.848, 0.766, and 0.653, respectively. Furthermore, a set of features was explored to evaluate the discourse structure of non-native spontaneous speech based on these annotations; the highest performing feature showed a correlation of 0.612 with scores of discourse coherence provided by expert human raters.
Evaluating C-RAN fronthaul functional splits in terms of network level energy and cost savings
(2016)

The placement of the complete baseband processing in a centralized pool results in high data rate requirement and inflexibility of the fronthaul network, which challenges the energy and cost effectiveness of the cloud radio access network (C-RAN). Recently, redesign of the C-RAN through functional split in the baseband processing chain has been proposed to overcome these challenges. This paper evaluates, by mathematical and simulation methods, different splits with respect to network level energy and cost efficiency having in the mind the expected quality of service. The proposed mathematical model quantifies the multiplexing gains and the trade-offs between centralization and decentralization concerning the cost of the pool, fronthaul network capacity and resource utilization. The event-based simulation captures the influence of the traffic load dynamics and traffic type variation on designing an efficient fronthaul network. Based on the obtained results, we derive a principle for fronthaul dimensioning based on the traffic profile. This principle allows for efficient radio access network with respect to multiplexing gains while achieving the expected users' quality of service.
Classes of Multiagent Q-learning Dynamics with epsilon-greedy Exploration
(2010)

Q-learning in single-agent environments is known to converge in the limit given sufficient exploration. The same algorithm has been applied, with some success, in multiagent environments, where traditional analysis techniques break down. Using established dynamical systems methods, we derive and study an idealization of Q-learning in 2-player 2-action repeated general-sum games. In particular, we address the discontinuous case of -greedy exploration and use it as a proxy for value-based algorithms to highlight a contrast with existing results in policy search. Analogously to previous results for gradient ascent algorithms, we provide a complete catalog of the convergence behavior of the -greedy Q-learning algorithm by introducing new subclasses of these games. We identify two subclasses of Prisoner’s Dilemma-like games where the application of Q-learning with -greedy exploration results in higher-than-Nash average payoffs for some initial conditions.
Behaviour Analysis of Multilayer Perceptrons with Multiple Hidden Neurons and Hidden Layers
(2011)

—The terms " Neural Network " (NN) and " Artificial Neural Network " (ANN) usually refer to a Multilayer Perceptron Network. It process the records one at a time, and "learn" by comparing their prediction of the record with the known actual record. The problem of model selection is considerably important for acquiring higher levels of generalization capability in supervised learning. This paper discussed behavioral analysis of different number of hidden layers and different number of hidden neurons. It's very difficult to select number of hidden layers and hidden neurons. There are different methods like Akaike's Information Criterion, Inverse test method and some traditional methods are used to find Neural Network architecture. What to do while neural network is not getting train or errors are not getting reduced. To reduce Neural Network errors, what we have to do with Neural Network architecture. These types of techniques are discussed and also discussed experiment and result. To solve different problems a neural network should be trained to perform correct classification..
Chemical crystal identification with deep learning machine vision
(2018)

This study was carried out with the purpose of testing the ability of deep learning machine vision to identify microscopic objects and geometries found in chemical crystal structures. A database of 6994 images taken with a light microscope showing microscopic crystal details of selected chemical compounds along with 180 images of an unknown chemical was created to train and test, respectively the deep learning models. The models used were GoogLeNet (22 layers deep network) and VGG-16 (16 layers deep network), based on the Caffe framework (University of California, Berkeley, CA) of the DIGITS platform (NVIDIA Corporation, Santa Clara, CA). The two models were successfully trained with the images, having validation accuracy values of 97.38% and 99.65% respectively. Finally, both models were able to correctly identify the unknown chemical sample with a high probability score of 93.34% (GoogLeNet) and 99.41% (VGG-16). The positive results found in this study can be further applied to other unknown sample identification tasks using light microscopy coupled with deep learning machine vision.
Opinion mining from student feedback data using supervised learning algorithms
(2016)

This paper explores opinion mining using supervised learning algorithms to find the polarity of the student feedback based on pre-defined features of teaching and learning. The study conducted involves the application of a combination of machine learning and natural language processing techniques on student feedback data gathered from module evaluation survey results of Middle East College, Oman. In addition to providing a step by step explanation of the process of implementation of opinion mining from student comments using the open source data analytics tool Rapid Miner, this paper also presents a comparative performance study of the algorithms like SVM, Naïve Bayes, K Nearest Neighbor and Neural Network classifier. The data set extracted from the survey is subjected to data preprocessing which is then used to train the algorithms for binomial classification. The trained models are also capable of predicting the polarity of the student comments based on extracted features like examination, teaching etc. The results are compared to find the better performance with respect to various evaluation criteria for the different algorithms.
Physical experience enhances science learning.
(2015)

Three laboratory experiments involving students' behavior and brain imaging and one randomized field experiment in a college physics class explored the importance of physical experience in science learning. We reasoned that students' understanding of science concepts such as torque and angular momentum is aided by activation of sensorimotor brain systems that add kinetic detail and meaning to students' thinking. We tested whether physical experience with angular momentum increases involvement of sensorimotor brain systems during students' subsequent reasoning and whether this involvement aids their understanding. The physical experience, a brief exposure to forces associated with angular momentum, significantly improved quiz scores. Moreover, improved performance was explained by activation of sensorimotor brain regions when students later reasoned about angular momentum. This finding specifies a mechanism underlying the value of physical experience in science education and leads the way for classroom practices in which experience with the physical world is an integral part of learning.